@inproceedings{c3dc4ede490c46139767e86b2bebe849,
title = "Building Type Classification with Incomplete Labels",
abstract = "Buildings can be distinguished by their form or function and maps of building types can be used by authorities for city planning. Training models to perform this classification requires appropriate training data. OpenStreetMap (OSM) data is globaly available and partly provides information on building types. However, this data can be incomplete or wrong. In this work a U-Net is trained to group buildings into one of the three major function classes (commercial/industrial, residential and other) using incomplete OSM data or ground-truth cadastral data. The model achieves overall accuracies of 72 and 75 percent. Given the OSM data has only around 20 percent of the ground truth labels this shows the incomplete data can be used to train for the building classification task.",
keywords = "Building-types, Cadastral, OSM, Remote-Sensing, Semantic Segmentation",
author = "Nikolai Skuppin and Hoffmann, {Eike Jens} and Yilei Shi and Zhu, {Xiao Xiang}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 ; Conference date: 17-07-2022 Through 22-07-2022",
year = "2022",
doi = "10.1109/IGARSS46834.2022.9884076",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5844--5847",
booktitle = "IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium",
}